import gym
import torch
import torch.nn.functional as F
import numpy as np
import matplotlib.pyplot as plt
from utils import rl_utils


class PolicyNet(torch.nn.Module):
    def __init__(self, state_dim, hidden_dim, action_dim):
        super(PolicyNet, self).__init__()
        self.fc1 = torch.nn.Linear(state_dim, hidden_dim)
        self.fc2 = torch.nn.Linear(hidden_dim, action_dim)

    def forward(self, x):
        x = F.tanh(self.fc1(x))
        return F.softmax(self.fc2(x), dim=1)


class ValueNet(torch.nn.Module):
    def __init__(self, state_dim, hidden_dim):
        super(ValueNet, self).__init__()
        self.fc1 = torch.nn.Linear(state_dim, hidden_dim)
        self.fc2 = torch.nn.Linear(hidden_dim, 1)

    def forward(self, x):
        x = F.tanh(self.fc1(x))
        return self.fc2(x)


class PPO:
    ''' PPO算法,采用截断方式 '''
    def __init__(self, state_dim, hidden_dim, action_dim, actor_lr, critic_lr,
                 lmbda, epochs, eps, gamma, device):
        self.actor = PolicyNet(state_dim, hidden_dim, action_dim).to(device)
        self.critic = ValueNet(state_dim, hidden_dim).to(device)
        self.actor_optimizer = torch.optim.Adam(self.actor.parameters(),
                                                lr=actor_lr)
        self.critic_optimizer = torch.optim.Adam(self.critic.parameters(),
                                                 lr=critic_lr)
        self.gamma = gamma
        self.lmbda = lmbda
        self.epochs = epochs  # 一条序列的数据用来训练轮数
        self.eps = eps  # PPO中截断范围的参数
        self.device = device

    def take_action(self, state):
        state = torch.tensor([state], dtype=torch.float).to(self.device)
        probs = self.actor(state)
        action_dist = torch.distributions.Categorical(probs)
        action = action_dist.sample()
        return action.item()

    def update(self, transition_dict):
        states = torch.tensor(transition_dict['states'],
                              dtype=torch.float).to(self.device)
        actions = torch.tensor(transition_dict['actions']).view(-1, 1).to(
            self.device)

        re_tmp = transition_dict['rewards']
        re_tmp = np.array(re_tmp)
        transition_dict['rewards'] = (re_tmp - re_tmp.mean()) / (re_tmp.std() + 1e-7)  #归一化奖励

        rewards = torch.tensor(transition_dict['rewards'],
                               dtype=torch.float).view(-1, 1).to(self.device)
        next_states = torch.tensor(transition_dict['next_states'],
                                   dtype=torch.float).to(self.device)
        dones = torch.tensor(transition_dict['dones'],
                             dtype=torch.float).view(-1, 1).to(self.device)
        td_target = rewards + self.gamma * self.critic(next_states) * (1 -
                                                                       dones)
        td_delta = td_target - self.critic(states)
        advantage = rl_utils.compute_advantage(self.gamma, self.lmbda,
                                               td_delta.cpu()).to(self.device)

        old_log_probs = torch.log(self.actor(states).gather(1,
                                                            actions)).detach()

        for _ in range(self.epochs):
            log_probs = torch.log(self.actor(states).gather(1, actions))
            ratio = torch.exp(log_probs - old_log_probs)
            surr1 = ratio * advantage
            surr2 = torch.clamp(ratio, 1 - self.eps,
                                1 + self.eps) * advantage  # 截断
            actor_loss = torch.mean(-torch.min(surr1, surr2))  # PPO损失函数
            critic_loss = torch.mean(
                F.mse_loss(self.critic(states), td_target.detach()))
            self.actor_optimizer.zero_grad()
            self.critic_optimizer.zero_grad()
            actor_loss.backward()
            critic_loss.backward()

            self.actor_optimizer.step()
            self.critic_optimizer.step()
            # print('---------')
            # for p in self.actor.parameters():
            #     print(p)
            # print('*********')
        print('acloss', actor_loss, 'closs:', critic_loss)

if __name__=='__main__':
    actor_lr = 1e-3
    critic_lr = 1e-2
    num_episodes = 500
    hidden_dim = 128
    gamma = 0.98
    lmbda = 0.95
    epochs = 10
    eps = 0.2
    device = torch.device("cuda") if torch.cuda.is_available() else torch.device(
        "cpu")

    env_name = 'CartPole-v0'
    env = gym.make(env_name)

    torch.manual_seed(1)
    state_dim = env.observation_space.shape[0]
    action_dim = env.action_space.n
    agent = PPO(state_dim, hidden_dim, action_dim, actor_lr, critic_lr, lmbda,
                epochs, eps, gamma, device)

    return_list = rl_utils.train_on_policy_agent(env, agent, num_episodes)